After M6.5 deletion of run_floating_stream and the frame dispatch,
WsFrameType.floating_request/floating_domain, WsFloatingRequest,
WsFloatingDomain, WsFloatingScope, WsDomain, and the StreamFormatter's
floating_domain branch were left as dead protocol surface. Remove them,
along with the corresponding test cases in test_schemas_v3.py and
test_output_formatter.py.
contextual_request + contextual_scope_update are the only WS
flows for ad-hoc contextual chat now. Floating system prompt
constant removed; Langfuse 'floating_system' is deleted in a
separate manual step. Also removes floating-agent LLM slot from
llm.py and the associated LLM_MODEL_FLOATING_AGENT setting entry.
Smoke trace 0b46841484ba7d024ed9f8d5ac8b1df0 showed the agent
defaulting to list_projects + get_project for a 'summarize
project Nexus' query, returning a shallow row without aiSummary
or tasks/notes. The legacy read tools were exposed via
*PROJECT_TOOLS / *TASK_TOOLS spreading.
Now _contextual_tools exposes exactly:
- get_page_details (sole read; supports per-entity + list views)
- create_task, update_task
- create_note
- create_timeline
Prompt rule 2 explicitly forbids the legacy reads, and the test
asserts they are excluded from the palette.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Use _build_system_prompt helper so the contextual agent gets the
same system-prompt slots as home/floating runners — most importantly
{date_context} so the agent can reason about due dates when
creating/updating tasks.
Also makes the session_id contract on run_contextual_stream explicit
(was reading via context['_debug']) and tightens the tool-list test.
_SessionBuffer.append_system_message(user_id, session_id, text) injects a
synthetic SystemMessage into the named session slot (creating it if absent).
ContextualBufferProxy closes over user_id + session_id so call sites need
only call proxy.append_system_message(text).
get_session_buffer(user_id, session_id, channel) in device_ws returns a
ContextualBufferProxy, keeping the test-patchable function signature intact.
New agent runner. Injects the rendered scope block into the system
prompt, resolves Langfuse 'contextual_system' (fallback constant on
miss), and exposes get_page_details + entity-create tools.
Note-edit tools (propose_note_edit) intentionally excluded — next sprint.
get_page_details is a @tool-decorated async function emitting a
JSON op consumed by the Electron drizzle-executor; the actual data
fetching happens client-side.
_contextual_tools() assembles the safe tool palette. Tools follow the
existing @tool decorator pattern from langchain_core.tools.
NOTE: test_run_contextual.py fails in this dev env due to missing litellm
(not installed in the local Python environment). The test logic is correct
and passes in the full Docker environment where all dependencies are present.
Add build_brief_multi_project_manifest() to deep_agent.py that fetches
all project folder manifests via execute_on_client and keeps the top 5
most-recently-modified files per project. Wire into run_home_brief in
brief_agent.py, injecting the <linked_folders> block into the system
prompt alongside FOLDER_TOOLS.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add optional project_id param to run_home_stream. When set, fetch the linked
folder manifest via _fetch_project_manifest and prepend the <linked_folder>
block to the system prompt. Also build an explicit tools list that extends
_all_tools_for_user with FOLDER_TOOLS so the home agent can read folder
files. device_ws._handle_home_request extracts project_id / projectId from
the home_request frame and forwards it to the runner.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add _fetch_project_manifest helper that calls read_project_folder_manifest
via execute_on_client. Wire it into run_task_brief_research_stream (new
optional project_id param) so the <linked_folder> block is prepended to the
system prompt when the task belongs to a linked project. Also bind
FOLDER_TOOLS into the task-brief tool palette so the agent can read folder
files. device_ws extracts project_id / projectId from the task_brief_request
frame and forwards it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add pypdf/python-docx deps, _extract_pdf_text/_extract_docx_text helpers,
and summarize_pdf/summarize_docx wrappers that delegate to summarize_text.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add _language_instruction() to deep_agent.py, reads language from core memory
- Append language directive to all 4 run_* functions (task/project/checkpoint/note)
- Minor fixes: alembic env, route imports, test cleanup
Langfuse V3 does not accept user_id/session_id on observation-level calls.
Moved to metadata dict in agent_runner, deep_agent, and agent_setup.
refactor(tests): fixture-based pattern for agent_runner_v2 eval tests
- cases.yaml + data/ fixtures under tests/fixtures/agent_runner_v2/
- pytest_generate_tests parametrizes test_eval_runner from YAML
- _resolve_projects() handles symbolic names and inline dicts
- _evaluate_case() centralizes all assertion logic
- --runner-dir CLI option for custom fixture folders
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Langfuse uses {{variable}} syntax in its prompt management UI, while the
hardcoded fallbacks use {variable} (Python str.format). The previous code
always called .format() which silently failed/errored when a real Langfuse
prompt was fetched.
- langfuse_client.py: add compile_prompt(template, prompt_obj, **vars)
→ uses prompt_obj.compile(**vars) when Langfuse is available
→ falls back to template.format(**vars) when using the hardcoded fallback
- agent_runner.py: replace .format() with compile_prompt() for
unified_processing (V2 local) and batch_cloud_processing (cloud agent)
- agent_setup.py: replace .format() with compile_prompt() for journey_system
deep_agent.py prompts have no variables, so no change needed there.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- New app/core/langfuse_client.py: lazy singleton client, get_prompt_or_fallback()
helper (returns raw template + prompt obj for linking), extract_usage() for token
counts. No-ops when LANGFUSE_* env vars are not set.
- deep_agent.py: home-agent and floating-agent runs wrapped in spans; each ainvoke
wrapped in a generation with model/input/output/usage; prompts fetched from
Langfuse (adiuva-home-agent, adiuva-floating-agent, adiuva-floating-classifier)
with hardcoded fallback.
- agent_runner.py: step1-classifier and step2-processor LLM calls traced; batch
agent _run_agent_with_tools spans + generations; cloud-processor included.
Prompts: adiuva-step1-classifier, adiuva-step2-processor, adiuva-cloud-processor.
- agent_setup.py: journey-setup span + generation per ainvoke; prompt_obj stored
on JourneySession and reused across turns. Prompt: journey_system.
- settings.py: LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, LANGFUSE_HOST added.
- .env.example: Langfuse section with EU/US/self-hosted host comments.
- requirements.txt: langfuse>=2.0.0.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Root causes fixed:
1. PROJECT_TOOLS removed from Step 2 tool set — project assignment is now
exclusively handled by the runner in code, never by the LLM.
2. When Step 1 returns "new", runner calls execute_on_client insert/projects
directly (before Step 2), gets the created id, and passes it as context.
3. Newly created projects are appended to the local `projects` list so that
subsequent files in the same run can match to them via Step 1 — prevents
one project per file when multiple files share the same topic.
Also add tests/test_classify_file.py with pytest cases for _classify_file
and a CLI runner: python -m tests.test_classify_file <file> [project...]
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Rewrite _STEP1_SYSTEM_PROMPT: lower matching threshold (no longer requires
"clear" match), strongly prefer existing projects over creating new ones,
use structured id=|name=|status= format with aiSummary for richer context
- Add code-level UUID validation: reject hallucinated ids not in the fetched
projects list, fall back to "new" instead of creating a bad link
- Rewrite _PROCESSING_SYSTEM_PROMPT: enforce explicit scan-before-create
process (read existing → search → update if found → create only if not)
with hard rule against calling create_* without checking existing records
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Replace LLM-driven triage with code-based directory scan and project fetch
- Two-step LLM approach: Step 1 classifies file→project+domains, Step 2 processes with tools
- Add domain descriptions to Step 1 prompt for better extraction accuracy
- Add _running_agents set for per-agent concurrency guard (one running instance per agent)
- Return 409 from route before DB write when agent already running
- Remove is_approved from task_agent create/update tools and system prompt
- Remove is_approved from timeline_agent create/update tools and system prompt
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- AgentTriggerRequest accepts optional agent_id (FE's stable electron-store UUID)
- _make_agent_executor injects run_context into every tool_call frame
so Electron can attribute actions to the correct agent run
- run_local_agent accepts run_context and sends a run_complete WS frame
when the run finishes so the FE can close the run record
- trigger_agent_run builds run_context with run_id=run_log.id and the
stable agent_id, passes it through to run_local_agent
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Remove unused config_schema from AgentCatalogItem (schema + route)
- Fix agent_setup system prompt: add extraction agent base behaviour
context so journey LLM knows what is already handled and focuses on
field mappings only; remove redundant data-types question (already
known from user selection); derive data types list dynamically
- Rewrite processing base prompt to use actual tool names
(list_tasks, update_task, add_task_comment, list_notes, update_note,
list_timelines, update_timeline, list_all_projects, create_project)
and enforce update-first strategy before falling back to creation
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace the single-pass FE-driven agent_run/agent_data flow with a
BE-orchestrated two-phase execution using LangChain tool-calling:
- Phase 1 (Triage): explores directory via new filesystem tools, matches
files to existing projects using PROJECT_TOOLS
- Phase 2 (Processing): reads files and performs CRUD per project group
with clean LLM context windows
Key changes:
- Add filesystem_agent.py with list_directory, read_file_content,
get_file_metadata tools using execute_on_client()
- Move setup journey from REST to WebSocket (journey_start/message frames)
- Add batch_runs_per_day billing limit and enforce in /trigger
- Remove deprecated agent_data/agent_complete frame handlers and queues
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
